
\section{Related Work}

The vast majority of paraphrasing work fall into two categories:
approaches based on the distributional hypothesis or on correspondences
between parallel
corpora~\cite{androutsopoulos2009survey,madnani2010generating}.

{\bf Using Distribution Similarity:} Lin and
Pantel's~\shortcite{lin2001discovery} DIRT employs mutual information
statistics to compute the similarity between relations represented in
dependency paths.  Resolver~\cite{yates2009unsupervised} introduces a
new similarity metric called the Extracted Shared Property (ESP) and
uses a probabilistic model to merge ESP with surface string
similarity.

Identifying the semantic equivalence of relation phrases is also
called {\it relation discovery} or {\it unsupervised semantic
  parsing}.  Often techniques don't compute the similarity explicitly
but rely implicitly on the distributional hypothesis. Poon and
Domingos~\shortcite{poon2009unsupervised} USP clusters relations
represented with fragments of dependency trees by repeatedly merging
relations having similar
context. Yao~\etal~\shortcite{yao2011structured,yaounsupervised}
introduces generative models for relation discovery using LDA-style
algorithm over a relation-feature matrix.
Chen~\etal~\shortcite{chen2011domain} focuses on domain-dependent
relation discovery, extending a generative model with meta-constraints
from lexical, syntactic and discourse regularities.

Our work solves a major problem with these approaches, avoiding errors
such as confusing synonyms with antonyms and causes with
effects. Furthermore, \sys\ doesn't require massive statistical
evidence as do most approaches based on the distributional hypothesis.



{\bf Using Parallel Corpora: } Comparable and parallel corpora,
including news streams and multiple translations of the same story,
have been used to generate paraphrases, both
sentential~\cite{barzilay2003learning,dolan2004unsupervised,shinyama2003paraphrase}
and
phrasal~\cite{barzilay2001extracting,shen2006adding,pang2003syntax}. Typical
methods first gather relevant articles and then pair sentences that
are potential paraphrases.  Given a training set of paraphrases,
models are learned and applied to unlabeled
pairs~\cite{dolan2005automatically,SocherEtAl2011:PoolRAE}. Phrasal
paraphrases are often obtained by running an alignment algorithm over
the paraphrased sentence pairs.

While prior work uses the temporal aspects of news streams as a coarse
filter, it largely relies on text metrics, such as context similarity and
edit distance, to make predictions and alignments. These metrics are
usually insufficient to produce high precision results; moreover they tend
to produce paraphrases that are simple lexical variants (\eg\ {\it \{go to,
go into\}.}). In contrast, \sys\ generates relation clusters with both high
precision and high diversity.

{\bf Others:}
Textual entailment~\cite{dagan2009recognizing}, which finds a phrase
implying another phrase, is closely related to the relation clustering task. Berant~\etal~\cite{berant2011global} notes the flaws in
distributional similarity and proposes local entailment
classifiers, which are able to combine many features.
Lin~\etal~\cite{lin2012no} also uses temporal information to detect
the semantics of entities.





